The present invention relates generally to methods for sensor registration, and more particularly to sensor registration via global optimization methods.
For measuring parameters in a system, multiple sensors may be deployed. For example, the temperature in a building complex may be monitored by installing thermometers throughout the complex. If the thermometers are read at different times, the temperature within the complex may be mapped as a function of location and time. In this example, three types of sensors are involved: a position sensor, a thermal sensor, and a time sensor. Depending on the system requirements, sensors may have a wide range of complexity. For example, a position sensor may be as simple as a tape measure, or as complex as a laser interferometer. Similarly, a thermal sensor may be as simple as a household thermometer, or as complex as an infrared imaging system.
For a measurement system comprising multiple sensors, a key process is sensor registration, which may be broadly construed as applying correction factors to sets of data measured by more than one sensor. Details of sensor registration are discussed below. One simple example of sensor registration is the synchronization of two clocks. At the same instant, if one clock reads 2:00 pm, and a second clock reads 2:05 pm, there is a 5 minute offset between the two. If the clocks are adjustable, they may be registered by adjusting them until they read the same; that is, the offset is set to zero. Alternatively, the offset may be corrected when the data is processed. If the first clock is used as a reference, 5 minutes are subtracted from the time read by the second clock. A second example of sensor registration is the calibration of two thermometers. At the same temperature, if one thermometer reads 110 degrees C. and a second thermometer reads 113 degrees C., there is a 3 degree offset between the two. If the thermometers have a calibration adjustment, the offset may be set to zero. Alternatively, in processing of the temperature data, if the first thermometer is used as a reference, 3 degrees may be subtracted from the reading of the second thermometer.
Some sensors measure a localized value. For example, a thermometer reads the temperature at the location of the thermometer. Other sensors sample values over a region. For example, an infrared camera measures temperatures over a region within its field of view. With a measurement system comprising a distributed array of infrared cameras, the temperature over an extended region may be mapped. Furthermore, more detailed characterization of the temperature variation over a localized region may be acquired if the field of view of multiple infrared cameras overlap. The temperature may be averaged over the readings from multiple cameras, for example. The process of aggregating data from multiple sensors is referred to as data fusion. Proper data fusion requires sensor registration. In the example of infrared cameras, temperature offsets among the different cameras need to be determined. Also, the relationship between the coordinates of a point measured relative to a specific infrared camera and the coordinates of the same point measured relative to a common reference coordinate system need to be determined. Furthermore, if the temperatures are measured as a function of time, and if times are read from a time stamp relative to an internal clock in each infrared camera, synchronization of the clocks to a common reference is required.
The number of sensors which a measurement system can accommodate is limited in part by the mode of data collection. For example, if temperature is measured by thermometers installed throughout a complex, the number of sensors may be limited by the number of stations that a technician can visit within a fixed period of time. With sensors which can transmit data remotely, however, complex measurement systems comprising large arrays of sensors connected via a network may be constructed. The size of the sensor array depends in part on the number of sensors measuring the same parameter at different locations (e.g., 100 infrared imaging systems placed at different locations) and in part on the number of sensors measuring different parameters at the same location (e.g., sensors to measure position, time, temperature, air pressure, and humidity). Therefore, a coherent method for registering an array of sensors, in which the array comprises an arbitrary number of sensors of arbitrary types, is advantageous. For dynamic measurement systems, sensor registration in near real time is further advantageous.
In many applications, measurements from multiple objects need to be collected. In one of the examples above, the temperature distribution of a building complex is monitored by placing thermometers throughout the complex. Here the temperatures at multiple locations are collected from multiple thermometers. In this instance, the thermometers are installed in fixed locations. For example, thermometer 1 is installed in room 101 on the first floor, and thermometer 2 is installed in room 201 on the second floor. The identities of the thermometers are well-known, and the probability of mis-identifying a thermometer is small. The probability is not zero, however, since a technician may make an error. For example, “thermometer 2, room 301” is erroneously entered into a database.
In other situations, identification of the objects may not be straightforward. This is especially true if the objects are mobile. Consider the tracking of aircraft by radar systems. For a commercial airliner, the pilot is in contact with air traffic controllers. The airliner broadcasts a unique identification number, and the identity of the plane is maintained as it is passed from one control tower to another. In a military situation, however, the identities of all the planes will probably not be known, especially for enemy aircraft. Distinguishing friendly aircraft from hostile aircraft is, of course, crucial.
In a complex measurement system, then, multiple sets of data are collected from multiple objects by multiple sensors. For correct characterization of the objects, the identification of the objects being measured by more than one sensor must be determined. For a single object being measured by more than one sensor, sensor registration errors need to be corrected. One approach to sensor registration between two sensors involves minimizing a likelihood function associating the measurements of the objects by two sensors. With this approach, sensor registration falls into the category of global optimization problems, which arise in a variety of science and engineering fields, as well as in other fields such as economics.
Existing methods for solving global optimization problems have various limitations, such as, requiring a good initial estimate of the optima or advance knowledge of the gradient of the function to be optimized. Also, some methods may converge on local minima instead of the global minimum. A coherent method for solving global optimization problems which do not have the above limitations are advantageous. Methods which are computationally efficient are further advantageous.